Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data.

JAMIA Open
Alon GevaKenneth D Mandl

Abstract

To advance use of real-world data (RWD) for pharmacovigilance, we sought to integrate a high-sensitivity natural language processing (NLP) pipeline for detecting potential adverse drug events (ADEs) with easily interpretable output for high-efficiency human review and adjudication of true ADEs. The adverse drug event presentation and tracking (ADEPT) system employs an open source NLP pipeline to identify in clinical notes mentions of medications and signs and symptoms potentially indicative of ADEs. ADEPT presents the output to human reviewers by highlighting these drug-event pairs within the context of the clinical note. To measure incidence of seizures associated with sildenafil, we applied ADEPT to 149 029 notes for 982 patients with pediatric pulmonary hypertension. Of 416 patients identified as taking sildenafil, NLP found 72 [17%, 95% confidence interval (CI) 14-21] with seizures as a potential ADE. Upon human review and adjudication, only 4 (0.96%, 95% CI 0.37-2.4) patients with seizures were determined to have true ADEs. Reviewers using ADEPT required a median of 89 s (interquartile range 57-142 s) per patient to review potential ADEs. ADEPT combines high throughput NLP to increase sensitivity of ADE detection and human...Continue Reading

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Software Mentioned

Apache
Java Cryptography Extension
ADEPT
R Foundation for Statistical
Splunk Enterprise
ADEPT Query Language ( SQL )
clinical Text Analysis Knowledge Extraction System ( cTAKES )
R
cTAKES
CTakesHit

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